An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …

The vision transformer (ViT) is a state-of-the-art architecture for image recognition tasks that
plays an important role in digital health applications. Medical images account for 90% of the …

Medical image segmentation plays an important role in computer-aided diagnosis. Attention
mechanisms that distinguish important parts from irrelevant parts have been widely used in …

This paper proposes a lightweight and efficient railway region extraction model LRseg,
which provides technical support for detecting foreign objects on the railway. LRseg consists …

Self-supervised learning (SSL) has recently achieved promising performance for 3D medical
image analysis tasks. Most current methods follow existing SSL paradigm originally …

Segmenting medical images is critical to facilitating both patient diagnoses and quantitative
research. A major limiting factor is the lack of labeled data, as obtaining expert annotations …

Acquiring pixel-level annotations is often limited in applications such as histology studies
that require domain expertise. Various semi-supervised learning approaches have been …

Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) have been pivotal in
biomedical image segmentation, yet their ability to manage long-range dependencies …

How can we test AI performance? This question seems trivial, but it isn't. Standard
benchmarks often have problems such as in-distribution and small-size test sets …

Abdominal tumor segmentation is a crucial yet challenging step during the screening and
diagnosis of tumors. While 3D segmentation models provide powerful performance, they …